Word error rate

About: Word error rate is a research topic. Over the lifetime, 11939 publications have been published within this topic receiving 298031 citations.

Evaluation of the performance of error-correcting codes on a Gilbert channel

Abstract: Presents a combinatorial analysis to derive a closed-form expression for the number of transmission errors that occur in a block transmitted through a Gilbert channel. This expression simplifies the computations needed to investigate the tradeoffs among the decoding error probability, degree of interleaving, and the error-correction ability of a code. The authors illustrate how a designer may apply the method to determine different combinations of the degree of interleaving and error correction ability to achieve a specified decoding error rate. >

Aggregating crowdsourced binary ratings

Abstract: In this paper we analyze a crowdsourcing system consisting of a set of users and a set of binary choice questions. Each user has an unknown, fixed, reliability that determines the user's error rate in answering questions. The problem is to determine the truth values of the questions solely based on the user answers. Although this problem has been studied extensively, theoretical error bounds have been shown only for restricted settings: when the graph between users and questions is either random or complete. In this paper we consider a general setting of the problem where the user--question graph can be arbitrary. We obtain bounds on the error rate of our algorithm and show it is governed by the expansion of the graph. We demonstrate, using several synthetic and real datasets, that our algorithm outperforms the state of the art.

Evaluation of Speaker Verification Security and Detection of HMM-Based Synthetic Speech

Abstract: In this paper, we evaluate the vulnerability of speaker verification (SV) systems to synthetic speech. The SV systems are based on either the Gaussian mixture model–universal background model (GMM-UBM) or support vector machine (SVM) using GMM supervectors. We use a hidden Markov model (HMM)-based text-to-speech (TTS) synthesizer, which can synthesize speech for a target speaker using small amounts of training data through model adaptation of an average voice or background model. Although the SV systems have a very low equal error rate (EER), when tested with synthetic speech generated from speaker models derived from the Wall Street Journal (WSJ) speech corpus, over 81% of the matched claims are accepted. This result suggests vulnerability in SV systems and thus a need to accurately detect synthetic speech. We propose a new feature based on relative phase shift (RPS), demonstrate reliable detection of synthetic speech, and show how this classifier can be used to improve security of SV systems.

Discriminative utterance verification for connected digits recognition

Abstract: Utterance verification represents an important technology in the design of user-friendly speech recognition systems It involves the recognition of keyword strings and the rejection of nonkeyword strings This paper describes a hidden Markov model-based (HMM-based) utterance verification system using the framework of statistical hypothesis testing The two major issues on how to design keyword and string scoring criteria are addressed For keyword verification, different alternative hypotheses are proposed based on the scores of antikeyword models and a general acoustic filler model For string verification, different measures are proposed with the objective of detecting nonvocabulary word strings and possibly erroneous strings (so-called putative errors) This paper also motivates the need for discriminative hypothesis testing in verification One such approach based on minimum classification error training is investigated in detail When the proposed verification technique was integrated into a state-of-the-art connected digit recognition system, the string error rate for valid digit strings was found to decrease by 57% when setting the rejection rate to 5% Furthermore, the system was able to correctly reject over 999% of nonvocabulary word strings

Robust speech recognition by normalization of the acoustic space

Abstract: Several algorithms are presented that increase the robustness of SPHINX, the CMU (Carnegie Mellon University) continuous-speech speaker-independent recognition systems, by normalizing the acoustic space via minimization of the overall VQ distortion. The authors propose an affine transformation of the cepstrum in which a matrix multiplication perform frequency normalization and a vector addition attempts environment normalization. The algorithms for environment normalization are efficient and improve the recognition accuracy when the system is tested on a microphone other than the one on which it was trained. The frequency normalization algorithm applies a different warping on the frequency axis to different speakers and it achieves a 10% decrease in error rate. >